{"id":"W2968093915","doi":"10.3390/en12163172","title":"Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Water Systems and Optimization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Research and Development Program of China","keywords":"Leak; SIGNAL (programming language); Pipeline (software); False alarm; Acoustics; Isolation (microbiology); Computer science; Detection theory; Pipeline transport; Constant false alarm rate; ALARM; Real-time computing; Engineering; Algorithm; Artificial intelligence; Physics; Telecommunications; Electrical engineering; Detector","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007038523,0.00006930425,0.00008493078,0.00008725043,0.00001679889,0.00001352204,0.00003524764,0.00005011841,0.000007267411],"category_scores_gemma":[0.00001309676,0.00006579996,0.0000115842,0.00009743179,0.000006280596,0.0001390791,0.000007086867,0.00003267135,0.000003677441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000189945,"about_ca_system_score_gemma":0.000006252332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002078217,"about_ca_topic_score_gemma":0.0001297911,"domain_scores_codex":[0.9996234,0.00001043916,0.0001382522,0.00007565672,0.00008662919,0.00006565357],"domain_scores_gemma":[0.9997427,0.0000203179,0.00003124235,0.0001331087,0.00005898577,0.00001361619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001179064,0.00000436278,0.0001014594,0.00008906781,0.00000287836,5.132133e-8,0.00007849473,0.9725963,0.02671526,0.00004864029,0.0001186815,0.0002330303],"study_design_scores_gemma":[0.0002418977,0.00004119362,0.0004486351,0.00003878556,0.000006920978,3.550159e-7,0.00001978976,0.9563916,0.04268542,0.000033101,0.0000251347,0.00006717751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3086967,0.00005931802,0.6895281,0.000008679339,0.0001377709,0.00007717891,0.000002246003,0.00007822629,0.001411755],"genre_scores_gemma":[0.9986429,0.000008239829,0.0009370996,0.000007792176,0.00003704253,0.000006584878,0.00001076085,0.00001396163,0.000335609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6899462,"threshold_uncertainty_score":0.2683244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005400928142259878,"score_gpt":0.1722208062968236,"score_spread":0.1668198781545637,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}